Meta-Analysis Workflow 008

Step 1: Study Description

Describe Your Research Study

Please provide a detailed description of what you want to study. Be as specific as possible about:

  • • The relationship or dependency you're looking for
  • • The parameters or variables you want to analyze
  • • The type of conclusions you want to make
  • • The population or context of your study
  • • Any specific outcomes or effects you're interested in

💡 Here is an example of a description (click to use and edit):

We aim to quantify the dose–response relationship between cumulative occupational silica exposure (mg/m³-years) and risk of silicosis progression, explicitly testing the hypothesis that risk depends on both exposure intensity and duration. The core parameters we will extract are individual-level exposure metrics, years since first exposure, smoking status, radiographic profusion category at baseline, and follow-up lung-function decline. By fitting random-effects meta-regression models we intend to conclude whether a clear exposure–response threshold exists and to derive a benchmark dose below which excess risk is negligible. The target population comprises underground miners and stone-crushers from high-income countries during 1990-2020, ensuring relatively homogeneous exposure assessment protocols. Our primary outcome is incident progressive massive fibrosis (PMF) diagnosed by ILO ≥2/2 radiographs; secondary outcomes include FEV₁ decline and all-cause mortality. We will stratify analyses by use of personal respirators to determine if protective equipment modifies the silica–PMF relationship. Subgroup meta-analyses will explore whether genetic polymorphisms in antioxidant enzymes alter individual susceptibility, moving toward personalized occupational health recommendations. Ultimately we seek to provide evidence-based exposure limits that regulators can adopt to reduce silicosis burden while balancing economic feasibility. A further conclusion will address whether smoking acts synergistically with silica or simply adds an independent background risk. By integrating longitudinal data from 15 cohorts we expect to generate precise, context-specific risk estimates that inform both clinical surveillance intervals and compensation policies.

💡 Tips for a good description:

  • • Be specific about the population you want to study
  • • Clearly state what interventions or exposures you want to compare
  • • Define the outcomes you're interested in measuring
  • • Mention any specific timeframes or contexts
  • • Include any particular study designs you want to focus on